{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Routing, speed imputation, and travel times\n",
    "\n",
    "Including parallelized shortest-path solving via built-in multiprocessing in OSMnx.\n",
    "\n",
    "Author: [Geoff Boeing](https://geoffboeing.com/)\n",
    "\n",
    "  - [Documentation](https://osmnx.readthedocs.io/)\n",
    "  - [Journal article and citation info](https://geoffboeing.com/publications/osmnx-paper/)\n",
    "  - [Code repository](https://github.com/gboeing/osmnx)\n",
    "  - [Examples gallery](https://github.com/gboeing/osmnx-examples)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:40:19.730969400Z",
     "start_time": "2025-03-08T07:40:16.174707300Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "'2.0.1'"
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import multiprocessing as mp\n",
    "\n",
    "import numpy as np\n",
    "import osmnx as ox\n",
    "\n",
    "np.random.seed(0)\n",
    "ox.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:01:12.439675200Z",
     "start_time": "2025-03-08T08:01:05.096114100Z"
    }
   },
   "outputs": [],
   "source": [
    "place = \"Piedmont, California, USA\"\n",
    "place = \"Nanjing, Jiangsu, China\"\n",
    "# Return type:  返回类型：networkx.MultiDiGraph  \n",
    "G = ox.graph.graph_from_place(place, network_type=\"drive\", custom_filter='[\"highway\"~\"motorway|trunk\"]')\n",
    "Gp = ox.projection.project_graph(G)"
   ]
  },
  {
   "cell_type": "code",
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Administrator\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\_overpass.py:267: UserWarning: This area is 69 times your configured Overpass max query area size. It will automatically be divided up into multiple sub-queries accordingly. This may take a long time.\n",
      "  multi_poly_proj = utils_geo._consolidate_subdivide_geometry(poly_proj)\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m                         Traceback (most recent call last)",
      "Cell \u001B[1;32mIn[16], line 3\u001B[0m\n\u001B[0;32m      1\u001B[0m place \u001B[38;5;241m=\u001B[39m \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mJiangsu, China\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m      2\u001B[0m \u001B[38;5;66;03m# Return type:  返回类型：networkx.MultiDiGraph  \u001B[39;00m\n\u001B[1;32m----> 3\u001B[0m C \u001B[38;5;241m=\u001B[39m ox\u001B[38;5;241m.\u001B[39mgraph\u001B[38;5;241m.\u001B[39mgraph_from_place(place, network_type\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mdrive\u001B[39m\u001B[38;5;124m\"\u001B[39m, custom_filter\u001B[38;5;241m=\u001B[39m\u001B[38;5;124m'\u001B[39m\u001B[38;5;124m[\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mhighway\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m~\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mmotorway|trunk\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124m]\u001B[39m\u001B[38;5;124m'\u001B[39m)\n\u001B[0;32m      4\u001B[0m Cp \u001B[38;5;241m=\u001B[39m ox\u001B[38;5;241m.\u001B[39mprojection\u001B[38;5;241m.\u001B[39mproject_graph(C)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\graph.py:391\u001B[0m, in \u001B[0;36mgraph_from_place\u001B[1;34m(query, network_type, simplify, retain_all, truncate_by_edge, which_result, custom_filter)\u001B[0m\n\u001B[0;32m    388\u001B[0m utils\u001B[38;5;241m.\u001B[39mlog(msg, level\u001B[38;5;241m=\u001B[39mlg\u001B[38;5;241m.\u001B[39mINFO)\n\u001B[0;32m    390\u001B[0m \u001B[38;5;66;03m# create graph using this polygon(s) geometry\u001B[39;00m\n\u001B[1;32m--> 391\u001B[0m G \u001B[38;5;241m=\u001B[39m graph_from_polygon(\n\u001B[0;32m    392\u001B[0m     polygon,\n\u001B[0;32m    393\u001B[0m     network_type\u001B[38;5;241m=\u001B[39mnetwork_type,\n\u001B[0;32m    394\u001B[0m     simplify\u001B[38;5;241m=\u001B[39msimplify,\n\u001B[0;32m    395\u001B[0m     retain_all\u001B[38;5;241m=\u001B[39mretain_all,\n\u001B[0;32m    396\u001B[0m     truncate_by_edge\u001B[38;5;241m=\u001B[39mtruncate_by_edge,\n\u001B[0;32m    397\u001B[0m     custom_filter\u001B[38;5;241m=\u001B[39mcustom_filter,\n\u001B[0;32m    398\u001B[0m )\n\u001B[0;32m    400\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mgraph_from_place returned graph with \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(G)\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m,\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m nodes and \u001B[39m\u001B[38;5;132;01m{\u001B[39;00m\u001B[38;5;28mlen\u001B[39m(G\u001B[38;5;241m.\u001B[39medges)\u001B[38;5;132;01m:\u001B[39;00m\u001B[38;5;124m,\u001B[39m\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m edges\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    401\u001B[0m utils\u001B[38;5;241m.\u001B[39mlog(msg, level\u001B[38;5;241m=\u001B[39mlg\u001B[38;5;241m.\u001B[39mINFO)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\graph.py:490\u001B[0m, in \u001B[0;36mgraph_from_polygon\u001B[1;34m(polygon, network_type, simplify, retain_all, truncate_by_edge, custom_filter)\u001B[0m\n\u001B[0;32m    488\u001B[0m \u001B[38;5;66;03m# create buffered graph from the downloaded data\u001B[39;00m\n\u001B[0;32m    489\u001B[0m bidirectional \u001B[38;5;241m=\u001B[39m network_type \u001B[38;5;129;01min\u001B[39;00m settings\u001B[38;5;241m.\u001B[39mbidirectional_network_types\n\u001B[1;32m--> 490\u001B[0m G_buff \u001B[38;5;241m=\u001B[39m _create_graph(response_jsons, bidirectional)\n\u001B[0;32m    492\u001B[0m \u001B[38;5;66;03m# truncate buffered graph to the buffered polygon and retain_all for\u001B[39;00m\n\u001B[0;32m    493\u001B[0m \u001B[38;5;66;03m# now. needed because overpass returns entire ways that also include\u001B[39;00m\n\u001B[0;32m    494\u001B[0m \u001B[38;5;66;03m# nodes outside the poly if the way (that is, a way with a single OSM\u001B[39;00m\n\u001B[0;32m    495\u001B[0m \u001B[38;5;66;03m# ID) has a node inside the poly at some point.\u001B[39;00m\n\u001B[0;32m    496\u001B[0m G_buff \u001B[38;5;241m=\u001B[39m truncate\u001B[38;5;241m.\u001B[39mtruncate_graph_polygon(G_buff, poly_buff, truncate_by_edge\u001B[38;5;241m=\u001B[39mtruncate_by_edge)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\graph.py:620\u001B[0m, in \u001B[0;36m_create_graph\u001B[1;34m(response_jsons, bidirectional)\u001B[0m\n\u001B[0;32m    616\u001B[0m \u001B[38;5;66;03m# consume response_jsons generator to download data from server. if\u001B[39;00m\n\u001B[0;32m    617\u001B[0m \u001B[38;5;66;03m# cache_only_mode, just consume response_jsons then continue next loop.\u001B[39;00m\n\u001B[0;32m    618\u001B[0m \u001B[38;5;66;03m# otherwise, extract nodes and paths from the downloaded OSM data.\u001B[39;00m\n\u001B[0;32m    619\u001B[0m response_count \u001B[38;5;241m=\u001B[39m \u001B[38;5;241m0\u001B[39m\n\u001B[1;32m--> 620\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m response_json \u001B[38;5;129;01min\u001B[39;00m response_jsons:\n\u001B[0;32m    621\u001B[0m     response_count \u001B[38;5;241m+\u001B[39m\u001B[38;5;241m=\u001B[39m \u001B[38;5;241m1\u001B[39m\n\u001B[0;32m    622\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m settings\u001B[38;5;241m.\u001B[39mcache_only_mode:\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\_overpass.py:397\u001B[0m, in \u001B[0;36m_download_overpass_network\u001B[1;34m(polygon, network_type, custom_filter)\u001B[0m\n\u001B[0;32m    395\u001B[0m \u001B[38;5;28;01mfor\u001B[39;00m way_filter \u001B[38;5;129;01min\u001B[39;00m way_filters:\n\u001B[0;32m    396\u001B[0m     query_str \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;132;01m{\u001B[39;00moverpass_settings\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m;(way\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mway_filter\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m(poly:\u001B[39m\u001B[38;5;132;01m{\u001B[39;00mpolygon_coord_str\u001B[38;5;132;01m!r}\u001B[39;00m\u001B[38;5;124m);>;);out;\u001B[39m\u001B[38;5;124m\"\u001B[39m\n\u001B[1;32m--> 397\u001B[0m     \u001B[38;5;28;01myield\u001B[39;00m _overpass_request(OrderedDict(data\u001B[38;5;241m=\u001B[39mquery_str))\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\osmnx\\_overpass.py:475\u001B[0m, in \u001B[0;36m_overpass_request\u001B[1;34m(data, pause, error_pause)\u001B[0m\n\u001B[0;32m    473\u001B[0m msg \u001B[38;5;241m=\u001B[39m \u001B[38;5;124mf\u001B[39m\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mPost \u001B[39m\u001B[38;5;132;01m{\u001B[39;00mprepared_url\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m with timeout=\u001B[39m\u001B[38;5;132;01m{\u001B[39;00msettings\u001B[38;5;241m.\u001B[39mrequests_timeout\u001B[38;5;132;01m}\u001B[39;00m\u001B[38;5;124m\"\u001B[39m\n\u001B[0;32m    474\u001B[0m utils\u001B[38;5;241m.\u001B[39mlog(msg, level\u001B[38;5;241m=\u001B[39mlg\u001B[38;5;241m.\u001B[39mINFO)\n\u001B[1;32m--> 475\u001B[0m response \u001B[38;5;241m=\u001B[39m requests\u001B[38;5;241m.\u001B[39mpost(\n\u001B[0;32m    476\u001B[0m     url,\n\u001B[0;32m    477\u001B[0m     data\u001B[38;5;241m=\u001B[39mdata,\n\u001B[0;32m    478\u001B[0m     timeout\u001B[38;5;241m=\u001B[39msettings\u001B[38;5;241m.\u001B[39mrequests_timeout,\n\u001B[0;32m    479\u001B[0m     headers\u001B[38;5;241m=\u001B[39m_http\u001B[38;5;241m.\u001B[39m_get_http_headers(),\n\u001B[0;32m    480\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39msettings\u001B[38;5;241m.\u001B[39mrequests_kwargs,\n\u001B[0;32m    481\u001B[0m )\n\u001B[0;32m    483\u001B[0m \u001B[38;5;66;03m# handle 429 and 504 errors by pausing then recursively re-trying request\u001B[39;00m\n\u001B[0;32m    484\u001B[0m \u001B[38;5;28;01mif\u001B[39;00m response\u001B[38;5;241m.\u001B[39mstatus_code \u001B[38;5;129;01min\u001B[39;00m {\u001B[38;5;241m429\u001B[39m, \u001B[38;5;241m504\u001B[39m}:  \u001B[38;5;66;03m# pragma: no cover\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\requests\\api.py:115\u001B[0m, in \u001B[0;36mpost\u001B[1;34m(url, data, json, **kwargs)\u001B[0m\n\u001B[0;32m    103\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21mpost\u001B[39m(url, data\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, json\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mNone\u001B[39;00m, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs):\n\u001B[0;32m    104\u001B[0m \u001B[38;5;250m    \u001B[39m\u001B[38;5;124mr\u001B[39m\u001B[38;5;124;03m\"\"\"Sends a POST request.\u001B[39;00m\n\u001B[0;32m    105\u001B[0m \n\u001B[0;32m    106\u001B[0m \u001B[38;5;124;03m    :param url: URL for the new :class:`Request` object.\u001B[39;00m\n\u001B[1;32m   (...)\u001B[0m\n\u001B[0;32m    112\u001B[0m \u001B[38;5;124;03m    :rtype: requests.Response\u001B[39;00m\n\u001B[0;32m    113\u001B[0m \u001B[38;5;124;03m    \"\"\"\u001B[39;00m\n\u001B[1;32m--> 115\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m request(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mpost\u001B[39m\u001B[38;5;124m\"\u001B[39m, url, data\u001B[38;5;241m=\u001B[39mdata, json\u001B[38;5;241m=\u001B[39mjson, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\requests\\api.py:59\u001B[0m, in \u001B[0;36mrequest\u001B[1;34m(method, url, **kwargs)\u001B[0m\n\u001B[0;32m     55\u001B[0m \u001B[38;5;66;03m# By using the 'with' statement we are sure the session is closed, thus we\u001B[39;00m\n\u001B[0;32m     56\u001B[0m \u001B[38;5;66;03m# avoid leaving sockets open which can trigger a ResourceWarning in some\u001B[39;00m\n\u001B[0;32m     57\u001B[0m \u001B[38;5;66;03m# cases, and look like a memory leak in others.\u001B[39;00m\n\u001B[0;32m     58\u001B[0m \u001B[38;5;28;01mwith\u001B[39;00m sessions\u001B[38;5;241m.\u001B[39mSession() \u001B[38;5;28;01mas\u001B[39;00m session:\n\u001B[1;32m---> 59\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m session\u001B[38;5;241m.\u001B[39mrequest(method\u001B[38;5;241m=\u001B[39mmethod, url\u001B[38;5;241m=\u001B[39murl, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\requests\\sessions.py:589\u001B[0m, in \u001B[0;36mSession.request\u001B[1;34m(self, method, url, params, data, headers, cookies, files, auth, timeout, allow_redirects, proxies, hooks, stream, verify, cert, json)\u001B[0m\n\u001B[0;32m    584\u001B[0m send_kwargs \u001B[38;5;241m=\u001B[39m {\n\u001B[0;32m    585\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mtimeout\u001B[39m\u001B[38;5;124m\"\u001B[39m: timeout,\n\u001B[0;32m    586\u001B[0m     \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mallow_redirects\u001B[39m\u001B[38;5;124m\"\u001B[39m: allow_redirects,\n\u001B[0;32m    587\u001B[0m }\n\u001B[0;32m    588\u001B[0m send_kwargs\u001B[38;5;241m.\u001B[39mupdate(settings)\n\u001B[1;32m--> 589\u001B[0m resp \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39msend(prep, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39msend_kwargs)\n\u001B[0;32m    591\u001B[0m \u001B[38;5;28;01mreturn\u001B[39;00m resp\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\requests\\sessions.py:703\u001B[0m, in \u001B[0;36mSession.send\u001B[1;34m(self, request, **kwargs)\u001B[0m\n\u001B[0;32m    700\u001B[0m start \u001B[38;5;241m=\u001B[39m preferred_clock()\n\u001B[0;32m    702\u001B[0m \u001B[38;5;66;03m# Send the request\u001B[39;00m\n\u001B[1;32m--> 703\u001B[0m r \u001B[38;5;241m=\u001B[39m adapter\u001B[38;5;241m.\u001B[39msend(request, \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mkwargs)\n\u001B[0;32m    705\u001B[0m \u001B[38;5;66;03m# Total elapsed time of the request (approximately)\u001B[39;00m\n\u001B[0;32m    706\u001B[0m elapsed \u001B[38;5;241m=\u001B[39m preferred_clock() \u001B[38;5;241m-\u001B[39m start\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\requests\\adapters.py:667\u001B[0m, in \u001B[0;36mHTTPAdapter.send\u001B[1;34m(self, request, stream, timeout, verify, cert, proxies)\u001B[0m\n\u001B[0;32m    664\u001B[0m     timeout \u001B[38;5;241m=\u001B[39m TimeoutSauce(connect\u001B[38;5;241m=\u001B[39mtimeout, read\u001B[38;5;241m=\u001B[39mtimeout)\n\u001B[0;32m    666\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 667\u001B[0m     resp \u001B[38;5;241m=\u001B[39m conn\u001B[38;5;241m.\u001B[39murlopen(\n\u001B[0;32m    668\u001B[0m         method\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mmethod,\n\u001B[0;32m    669\u001B[0m         url\u001B[38;5;241m=\u001B[39murl,\n\u001B[0;32m    670\u001B[0m         body\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mbody,\n\u001B[0;32m    671\u001B[0m         headers\u001B[38;5;241m=\u001B[39mrequest\u001B[38;5;241m.\u001B[39mheaders,\n\u001B[0;32m    672\u001B[0m         redirect\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m    673\u001B[0m         assert_same_host\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m    674\u001B[0m         preload_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m    675\u001B[0m         decode_content\u001B[38;5;241m=\u001B[39m\u001B[38;5;28;01mFalse\u001B[39;00m,\n\u001B[0;32m    676\u001B[0m         retries\u001B[38;5;241m=\u001B[39m\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mmax_retries,\n\u001B[0;32m    677\u001B[0m         timeout\u001B[38;5;241m=\u001B[39mtimeout,\n\u001B[0;32m    678\u001B[0m         chunked\u001B[38;5;241m=\u001B[39mchunked,\n\u001B[0;32m    679\u001B[0m     )\n\u001B[0;32m    681\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (ProtocolError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m err:\n\u001B[0;32m    682\u001B[0m     \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m(err, request\u001B[38;5;241m=\u001B[39mrequest)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\urllib3\\connectionpool.py:793\u001B[0m, in \u001B[0;36mHTTPConnectionPool.urlopen\u001B[1;34m(self, method, url, body, headers, retries, redirect, assert_same_host, timeout, pool_timeout, release_conn, chunked, body_pos, preload_content, decode_content, **response_kw)\u001B[0m\n\u001B[0;32m    790\u001B[0m response_conn \u001B[38;5;241m=\u001B[39m conn \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m release_conn \u001B[38;5;28;01melse\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m\n\u001B[0;32m    792\u001B[0m \u001B[38;5;66;03m# Make the request on the HTTPConnection object\u001B[39;00m\n\u001B[1;32m--> 793\u001B[0m response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_make_request(\n\u001B[0;32m    794\u001B[0m     conn,\n\u001B[0;32m    795\u001B[0m     method,\n\u001B[0;32m    796\u001B[0m     url,\n\u001B[0;32m    797\u001B[0m     timeout\u001B[38;5;241m=\u001B[39mtimeout_obj,\n\u001B[0;32m    798\u001B[0m     body\u001B[38;5;241m=\u001B[39mbody,\n\u001B[0;32m    799\u001B[0m     headers\u001B[38;5;241m=\u001B[39mheaders,\n\u001B[0;32m    800\u001B[0m     chunked\u001B[38;5;241m=\u001B[39mchunked,\n\u001B[0;32m    801\u001B[0m     retries\u001B[38;5;241m=\u001B[39mretries,\n\u001B[0;32m    802\u001B[0m     response_conn\u001B[38;5;241m=\u001B[39mresponse_conn,\n\u001B[0;32m    803\u001B[0m     preload_content\u001B[38;5;241m=\u001B[39mpreload_content,\n\u001B[0;32m    804\u001B[0m     decode_content\u001B[38;5;241m=\u001B[39mdecode_content,\n\u001B[0;32m    805\u001B[0m     \u001B[38;5;241m*\u001B[39m\u001B[38;5;241m*\u001B[39mresponse_kw,\n\u001B[0;32m    806\u001B[0m )\n\u001B[0;32m    808\u001B[0m \u001B[38;5;66;03m# Everything went great!\u001B[39;00m\n\u001B[0;32m    809\u001B[0m clean_exit \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\urllib3\\connectionpool.py:537\u001B[0m, in \u001B[0;36mHTTPConnectionPool._make_request\u001B[1;34m(self, conn, method, url, body, headers, retries, timeout, chunked, response_conn, preload_content, decode_content, enforce_content_length)\u001B[0m\n\u001B[0;32m    535\u001B[0m \u001B[38;5;66;03m# Receive the response from the server\u001B[39;00m\n\u001B[0;32m    536\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 537\u001B[0m     response \u001B[38;5;241m=\u001B[39m conn\u001B[38;5;241m.\u001B[39mgetresponse()\n\u001B[0;32m    538\u001B[0m \u001B[38;5;28;01mexcept\u001B[39;00m (BaseSSLError, \u001B[38;5;167;01mOSError\u001B[39;00m) \u001B[38;5;28;01mas\u001B[39;00m e:\n\u001B[0;32m    539\u001B[0m     \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_raise_timeout(err\u001B[38;5;241m=\u001B[39me, url\u001B[38;5;241m=\u001B[39murl, timeout_value\u001B[38;5;241m=\u001B[39mread_timeout)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\site-packages\\urllib3\\connection.py:466\u001B[0m, in \u001B[0;36mHTTPConnection.getresponse\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    463\u001B[0m \u001B[38;5;28;01mfrom\u001B[39;00m \u001B[38;5;21;01m.\u001B[39;00m\u001B[38;5;21;01mresponse\u001B[39;00m \u001B[38;5;28;01mimport\u001B[39;00m HTTPResponse\n\u001B[0;32m    465\u001B[0m \u001B[38;5;66;03m# Get the response from http.client.HTTPConnection\u001B[39;00m\n\u001B[1;32m--> 466\u001B[0m httplib_response \u001B[38;5;241m=\u001B[39m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mgetresponse()\n\u001B[0;32m    468\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m    469\u001B[0m     assert_header_parsing(httplib_response\u001B[38;5;241m.\u001B[39mmsg)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\http\\client.py:1430\u001B[0m, in \u001B[0;36mHTTPConnection.getresponse\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m   1428\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1429\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m-> 1430\u001B[0m         response\u001B[38;5;241m.\u001B[39mbegin()\n\u001B[0;32m   1431\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m \u001B[38;5;167;01mConnectionError\u001B[39;00m:\n\u001B[0;32m   1432\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mclose()\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\http\\client.py:331\u001B[0m, in \u001B[0;36mHTTPResponse.begin\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    329\u001B[0m \u001B[38;5;66;03m# read until we get a non-100 response\u001B[39;00m\n\u001B[0;32m    330\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[1;32m--> 331\u001B[0m     version, status, reason \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_read_status()\n\u001B[0;32m    332\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m status \u001B[38;5;241m!=\u001B[39m CONTINUE:\n\u001B[0;32m    333\u001B[0m         \u001B[38;5;28;01mbreak\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\http\\client.py:292\u001B[0m, in \u001B[0;36mHTTPResponse._read_status\u001B[1;34m(self)\u001B[0m\n\u001B[0;32m    291\u001B[0m \u001B[38;5;28;01mdef\u001B[39;00m \u001B[38;5;21m_read_status\u001B[39m(\u001B[38;5;28mself\u001B[39m):\n\u001B[1;32m--> 292\u001B[0m     line \u001B[38;5;241m=\u001B[39m \u001B[38;5;28mstr\u001B[39m(\u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mfp\u001B[38;5;241m.\u001B[39mreadline(_MAXLINE \u001B[38;5;241m+\u001B[39m \u001B[38;5;241m1\u001B[39m), \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124miso-8859-1\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n\u001B[0;32m    293\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m \u001B[38;5;28mlen\u001B[39m(line) \u001B[38;5;241m>\u001B[39m _MAXLINE:\n\u001B[0;32m    294\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m LineTooLong(\u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mstatus line\u001B[39m\u001B[38;5;124m\"\u001B[39m)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\socket.py:720\u001B[0m, in \u001B[0;36mSocketIO.readinto\u001B[1;34m(self, b)\u001B[0m\n\u001B[0;32m    718\u001B[0m \u001B[38;5;28;01mwhile\u001B[39;00m \u001B[38;5;28;01mTrue\u001B[39;00m:\n\u001B[0;32m    719\u001B[0m     \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[1;32m--> 720\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sock\u001B[38;5;241m.\u001B[39mrecv_into(b)\n\u001B[0;32m    721\u001B[0m     \u001B[38;5;28;01mexcept\u001B[39;00m timeout:\n\u001B[0;32m    722\u001B[0m         \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_timeout_occurred \u001B[38;5;241m=\u001B[39m \u001B[38;5;28;01mTrue\u001B[39;00m\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\ssl.py:1251\u001B[0m, in \u001B[0;36mSSLSocket.recv_into\u001B[1;34m(self, buffer, nbytes, flags)\u001B[0m\n\u001B[0;32m   1247\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m flags \u001B[38;5;241m!=\u001B[39m \u001B[38;5;241m0\u001B[39m:\n\u001B[0;32m   1248\u001B[0m         \u001B[38;5;28;01mraise\u001B[39;00m \u001B[38;5;167;01mValueError\u001B[39;00m(\n\u001B[0;32m   1249\u001B[0m           \u001B[38;5;124m\"\u001B[39m\u001B[38;5;124mnon-zero flags not allowed in calls to recv_into() on \u001B[39m\u001B[38;5;132;01m%s\u001B[39;00m\u001B[38;5;124m\"\u001B[39m \u001B[38;5;241m%\u001B[39m\n\u001B[0;32m   1250\u001B[0m           \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m\u001B[38;5;18m__class__\u001B[39m)\n\u001B[1;32m-> 1251\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39mread(nbytes, buffer)\n\u001B[0;32m   1252\u001B[0m \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1253\u001B[0m     \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28msuper\u001B[39m()\u001B[38;5;241m.\u001B[39mrecv_into(buffer, nbytes, flags)\n",
      "File \u001B[1;32m~\\anaconda3\\envs\\ox\\Lib\\ssl.py:1103\u001B[0m, in \u001B[0;36mSSLSocket.read\u001B[1;34m(self, len, buffer)\u001B[0m\n\u001B[0;32m   1101\u001B[0m \u001B[38;5;28;01mtry\u001B[39;00m:\n\u001B[0;32m   1102\u001B[0m     \u001B[38;5;28;01mif\u001B[39;00m buffer \u001B[38;5;129;01mis\u001B[39;00m \u001B[38;5;129;01mnot\u001B[39;00m \u001B[38;5;28;01mNone\u001B[39;00m:\n\u001B[1;32m-> 1103\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sslobj\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;28mlen\u001B[39m, buffer)\n\u001B[0;32m   1104\u001B[0m     \u001B[38;5;28;01melse\u001B[39;00m:\n\u001B[0;32m   1105\u001B[0m         \u001B[38;5;28;01mreturn\u001B[39;00m \u001B[38;5;28mself\u001B[39m\u001B[38;5;241m.\u001B[39m_sslobj\u001B[38;5;241m.\u001B[39mread(\u001B[38;5;28mlen\u001B[39m)\n",
      "\u001B[1;31mKeyboardInterrupt\u001B[0m: "
     ]
    }
   ],
   "source": [
    "place = \"Jiangsu, China\"\n",
    "# Return type:  返回类型：networkx.MultiDiGraph  \n",
    "C = ox.graph.graph_from_place(place, network_type=\"drive\", custom_filter='[\"highway\"~\"motorway|trunk\"]')\n",
    "Cp = ox.projection.project_graph(C)"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2025-03-08T08:00:58.035839800Z",
     "start_time": "2025-03-08T07:57:59.398307500Z"
    }
   },
   "execution_count": 16
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Fast nearest node/edge search with OSMnx\n",
    "\n",
    "The nearest_nodes and nearest_edges functions take arrays of x and y (or lng/lat) coordinates and return the nearest node/edge to each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:43:24.825175500Z",
     "start_time": "2025-03-08T07:43:13.882403100Z"
    }
   },
   "outputs": [],
   "source": [
    "# randomly sample n points spatially-constrained to the network's geometry\n",
    "points = ox.utils_geo.sample_points(ox.convert.to_undirected(Gp), n=100)\n",
    "X = points.x.values\n",
    "Y = points.y.values\n",
    "X0 = X.mean()\n",
    "Y0 = Y.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:43:26.850599Z",
     "start_time": "2025-03-08T07:43:26.741591900Z"
    }
   },
   "outputs": [],
   "source": [
    "# find each nearest node to several points, and optionally return distance\n",
    "nodes, dists = ox.distance.nearest_nodes(Gp, X, Y, return_dist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:43:34.093900700Z",
     "start_time": "2025-03-08T07:43:33.969892600Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "4219267438"
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# or, find the nearest node to a single point\n",
    "node = ox.distance.nearest_nodes(Gp, X0, Y0)\n",
    "node"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:43:37.471970500Z",
     "start_time": "2025-03-08T07:43:35.856858Z"
    }
   },
   "outputs": [],
   "source": [
    "# find each nearest edge to several points, and optionally return distance\n",
    "edges, dists = ox.distance.nearest_edges(Gp, X, Y, return_dist=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T07:43:39.082087100Z",
     "start_time": "2025-03-08T07:43:37.470972700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "(4219267438, 4219268218, 0)"
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the nearest edge to a single point\n",
    "edge = ox.distance.nearest_edges(Gp, X0, Y0)\n",
    "edge"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Basic routing by distance\n",
    "\n",
    "Pick two nodes. Then find the shortest path between origin and destination, using weight='length' to find the shortest path by minimizing distance traveled (otherwise it treats each edge as weight=1)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:03:16.665219700Z",
     "start_time": "2025-03-08T08:03:16.308171800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MultiDiGraph with 2202 nodes and 2713 edges\n"
     ]
    }
   ],
   "source": [
    "# find the shortest path (by distance) between these nodes then plot it\n",
    "orig = list(G)[0]\n",
    "dest = list(G)[120]\n",
    "route = ox.routing.shortest_path(G, orig, dest, weight=\"length\")\n",
    "fig, ax = ox.plot.plot_graph_route(G, route, route_color=\"y\", route_linewidth=6, node_size=0)\n",
    "print(G)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Or get *k* shortest paths, weighted by some attribute:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:02:17.746681400Z",
     "start_time": "2025-03-08T08:02:16.067552Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "routes = ox.routing.k_shortest_paths(G, orig, dest, k=30, weight=\"length\")\n",
    "fig, ax = ox.plot.plot_graph_routes(\n",
    "    G, list(routes), route_colors=\"y\", route_linewidth=4, node_size=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Imputing travel speeds and times\n",
    "\n",
    "The `add_edge_speeds` function add edge speeds (km per hour) to graph as new `speed_kph` edge attributes. Imputes free-flow travel speeds for all edges based on mean `maxspeed` value of edges, per highway type. This mean-imputation can obviously be imprecise, and the caller can override it by passing in `hwy_speeds` and/or `fallback` arguments that correspond to local speed limit standards. See docstring for details."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:04:29.644567900Z",
     "start_time": "2025-03-08T08:04:29.518555900Z"
    }
   },
   "outputs": [],
   "source": [
    "# impute speed on all edges missing data\n",
    "G = ox.routing.add_edge_speeds(G)\n",
    "\n",
    "# calculate travel time (seconds) for all edges\n",
    "G = ox.routing.add_edge_travel_times(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:04:32.103755500Z",
     "start_time": "2025-03-08T08:04:32.010749100Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "                                 length  speed_kph  travel_time\nhighway                                                        \n['motorway_link', 'motorway']    1493.6       44.0        122.3\n['motorway_link', 'trunk']       1553.2       44.0        127.2\n['motorway_link', 'trunk_link']   427.1       44.0         35.0\n['trunk', 'motorway']            1745.1      108.6         57.2\n['trunk', 'motorway_link']       1909.5       85.8         80.1\n['trunk', 'trunk_link']          1139.5       84.9         48.8\nmotorway                         2090.3      113.5         66.0\nmotorway_link                     521.8       44.0         42.8\ntrunk                            1435.8       85.7         60.7\ntrunk_link                        344.6       81.1         15.3",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>length</th>\n      <th>speed_kph</th>\n      <th>travel_time</th>\n    </tr>\n    <tr>\n      <th>highway</th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>['motorway_link', 'motorway']</th>\n      <td>1493.6</td>\n      <td>44.0</td>\n      <td>122.3</td>\n    </tr>\n    <tr>\n      <th>['motorway_link', 'trunk']</th>\n      <td>1553.2</td>\n      <td>44.0</td>\n      <td>127.2</td>\n    </tr>\n    <tr>\n      <th>['motorway_link', 'trunk_link']</th>\n      <td>427.1</td>\n      <td>44.0</td>\n      <td>35.0</td>\n    </tr>\n    <tr>\n      <th>['trunk', 'motorway']</th>\n      <td>1745.1</td>\n      <td>108.6</td>\n      <td>57.2</td>\n    </tr>\n    <tr>\n      <th>['trunk', 'motorway_link']</th>\n      <td>1909.5</td>\n      <td>85.8</td>\n      <td>80.1</td>\n    </tr>\n    <tr>\n      <th>['trunk', 'trunk_link']</th>\n      <td>1139.5</td>\n      <td>84.9</td>\n      <td>48.8</td>\n    </tr>\n    <tr>\n      <th>motorway</th>\n      <td>2090.3</td>\n      <td>113.5</td>\n      <td>66.0</td>\n    </tr>\n    <tr>\n      <th>motorway_link</th>\n      <td>521.8</td>\n      <td>44.0</td>\n      <td>42.8</td>\n    </tr>\n    <tr>\n      <th>trunk</th>\n      <td>1435.8</td>\n      <td>85.7</td>\n      <td>60.7</td>\n    </tr>\n    <tr>\n      <th>trunk_link</th>\n      <td>344.6</td>\n      <td>81.1</td>\n      <td>15.3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# see mean speed/time values by road type\n",
    "edges = ox.convert.graph_to_gdfs(G, nodes=False)\n",
    "edges[\"highway\"] = edges[\"highway\"].astype(str)\n",
    "edges.groupby(\"highway\")[[\"length\", \"speed_kph\", \"travel_time\"]].mean().round(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:04:57.787728600Z",
     "start_time": "2025-03-08T08:04:57.659720300Z"
    }
   },
   "outputs": [],
   "source": [
    "# same thing again, but this time pass in a few default speed values (km/hour)\n",
    "# to fill in edges with missing `maxspeed` from OSM\n",
    "hwy_speeds = {\"residential\": 35, \"secondary\": 50, \"tertiary\": 60}\n",
    "G = ox.routing.add_edge_speeds(G, hwy_speeds=hwy_speeds)\n",
    "G = ox.routing.add_edge_travel_times(G)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:05:08.649563300Z",
     "start_time": "2025-03-08T08:05:08.617553300Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate two routes by minimizing travel distance vs travel time\n",
    "orig = list(G)[1]\n",
    "dest = list(G)[120]\n",
    "route1 = ox.routing.shortest_path(G, orig, dest, weight=\"length\")\n",
    "route2 = ox.routing.shortest_path(G, orig, dest, weight=\"travel_time\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:05:11.296770900Z",
     "start_time": "2025-03-08T08:05:10.897729800Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot the routes\n",
    "fig, ax = ox.plot.plot_graph_routes(\n",
    "    G, routes=[route1, route2], route_colors=[\"r\", \"y\"], route_linewidth=6, node_size=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:05:37.127741700Z",
     "start_time": "2025-03-08T08:05:37.081739100Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Route 1 is 40796 meters and takes 1425 seconds.\n",
      "Route 2 is 40796 meters and takes 1425 seconds.\n"
     ]
    }
   ],
   "source": [
    "# compare the two routes\n",
    "route1_length = int(sum(ox.routing.route_to_gdf(G, route1, weight=\"length\")[\"length\"]))\n",
    "route2_length = int(sum(ox.routing.route_to_gdf(G, route2, weight=\"travel_time\")[\"length\"]))\n",
    "route1_time = int(sum(ox.routing.route_to_gdf(G, route1, weight=\"length\")[\"travel_time\"]))\n",
    "route2_time = int(sum(ox.routing.route_to_gdf(G, route2, weight=\"travel_time\")[\"travel_time\"]))\n",
    "print(\"Route 1 is\", route1_length, \"meters and takes\", route1_time, \"seconds.\")\n",
    "print(\"Route 2 is\", route2_length, \"meters and takes\", route2_time, \"seconds.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The yellow route minimizes travel time, and is thus longer but faster than the red route.\n",
    "\n",
    "For more examples of travel time, see the [isochrones example](13-isolines-isochrones.ipynb).\n",
    "\n",
    "For more examples of routing, including using elevation as an impedance, see the [elevations example](12-node-elevations-edge-grades.ipynb)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. Multiprocessing\n",
    "\n",
    "Calculating lots of shortest paths can be slow, but OSMnx has built-in shortest path solver parallelization and multiprocessing. With the `shortest_path` function, you can pass in a single origin-destination pair to solve the one shortest path, or you can pass in lists of origins and destinations to solve each shortest path between the pairs. If you're solving shortest paths for multiple origins/destinations, the `cpus` argument determines how many CPU cores to utilize for parallelized solving. Multiprocessing adds some overhead, so it's only faster if you're solving a lot of paths. It also has substantial RAM requirements (as it must copy the graph into each sub-process), so be careful with your RAM when setting the `cpus` argument."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:05:52.793931900Z",
     "start_time": "2025-03-08T08:05:52.769932500Z"
    }
   },
   "outputs": [],
   "source": [
    "# calculate 100,000 shortest-path routes using random origin-destination pairs\n",
    "n = 100000\n",
    "origs = np.random.choice(G.nodes, size=n, replace=True)\n",
    "dests = np.random.choice(G.nodes, size=n, replace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many CPUs do you have\n",
    "mp.cpu_count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# it takes 2.3 seconds to solve all the routes using all the cores on my computer\n",
    "# I have a 24-thread AMD 5900x: performance will depend on your specific CPU\n",
    "# routes = ox.routing.shortest_path(G, origs, dests, weight=\"travel_time\", cpus=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "%%time\n",
    "# it takes 29 seconds to solve all the routes using just 1 core on my computer\n",
    "routes = ox.routing.shortest_path(G, origs, dests, weight=\"travel_time\", cpus=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# how many total results did we get\n",
    "print(len(routes))\n",
    "\n",
    "# and how many were solvable paths\n",
    "# some will be unsolvable due to directed graph perimeter effects\n",
    "routes_valid = [r for r in routes if r is not None]\n",
    "print(len(routes_valid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Miscellaneous routing notes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The routing correctly handles one-way streets:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:06:57.279449700Z",
     "start_time": "2025-03-08T08:06:47.832789700Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "G2 = ox.graph.graph_from_address(\n",
    "    \"N. Sicily Pl., Chandler, Arizona\",\n",
    "    dist=800,\n",
    "    network_type=\"drive\",\n",
    "    truncate_by_edge=True,\n",
    ")\n",
    "origin = (33.307792, -111.894940)\n",
    "destination = (33.312994, -111.894998)\n",
    "origin_node = ox.distance.nearest_nodes(G2, origin[1], origin[0])\n",
    "destination_node = ox.distance.nearest_nodes(G2, destination[1], destination[0])\n",
    "route = ox.routing.shortest_path(G2, origin_node, destination_node)\n",
    "fig, ax = ox.plot.plot_graph_route(G2, route, route_color=\"c\", node_size=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, when there are parallel edges between nodes in the route, OSMnx picks the shortest edge to plot:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-03-08T08:07:03.914916400Z",
     "start_time": "2025-03-08T08:06:57.281457200Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 800x800 with 1 Axes>",
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "location_point = (33.299896, -111.831638)\n",
    "G2 = ox.graph.graph_from_point(location_point, dist=400, truncate_by_edge=True)\n",
    "origin = (33.301821, -111.829871)\n",
    "destination = (33.301402, -111.833108)\n",
    "origin_node = ox.distance.nearest_nodes(G2, origin[1], origin[0])\n",
    "destination_node = ox.distance.nearest_nodes(G2, destination[1], destination[0])\n",
    "route = ox.routing.shortest_path(G2, origin_node, destination_node)\n",
    "fig, ax = ox.plot.plot_graph_route(G2, route, route_color=\"c\", node_size=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "name": "python3",
   "language": "python",
   "display_name": "Python 3 (ipykernel)"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
